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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sistem Informasi dan Sistem Komputer Jurnal Teknik Informatika (JUTIF) Jurnal INFOTEL
Dewa, Chandra Kusuma
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Other

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Modeling OTP Delivery Notification Status through a Causality Bayesian Network Asriny, Novendri Isra; Dewa, Chandra Kusuma; Luthfi, Ahmat
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90030

Abstract

Digital money is the fundamental driving factor behind today's modern economy. Credit/debit cards, e-wallets, and other contactless payment options are widely available nowadays. This also raises the security risk associated with passwords in online transactions. One-time passwords (OTPs) are another option for mitigating this. A one-time password (OTP) serves as an additional password authentication or validation technique for each user authentication session. Failures in transmitting OTP passwords through SMS can arise owing to operator network faults or technological concerns.To minimize the risk value that arises in online transactions, it is necessary to evaluate the causality of the OTP SMS sending transaction status category by determining the main factors for successful OTP SMS sending and identifying the causes of failure when sending OTP SMS using the Bayesian Network method. According to data analysis, online transactions occur more frequently in the morning, with status summaries such as no delay, unknown status, and others. Furthermore, there is causality with at least three variables in the principal status summary, including no delay, uncertain summary, long delay, normal, likely operator issues, abnormal, and more. With a high accuracy rate of around 90% in forecasting the likelihood of recurrence.
Aspect-Based Sentiment Analysis Pada Aplikasi Pelacakan Kasus Covid-19 (Studi Kasus: Pedulilindungi) Suryatin, Suryatin; Fudholi, Dhomas Hatta; Dewa, Chandra Kusuma; Iman, Nur
Jurnal Sistem Informasi dan Sistem Komputer Vol 9 No 1 (2024): Vol 9 No 1 - 2024
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v9i1.304

Abstract

Berbagi pengalaman melalui internet dan media sosial dapat menunjukan sikap dan perasaan dalam bentuk umpan balik. Aplikasi publik yang banyak disoroti pada masa wabah corona virus yaitu aplikasi pedulilindungi yang merupakan aplikasi monitoring perkembangan Corona Virus Disease di Indonesia. Salah satu fenomena timbulnya Aspect-based sentiment dalam pada prilaku sentimentil masyarakat terhadap layanan aplikasi pedulilindungi. Penelitian ini bertujuan untuk mengetahui besarnya nilai sentimen pada layanan pedulilindungi dan berfokus pada aspect based sentiment analysis (ABSA) pada domain ulasan aplikasi pemerintah. Analisis terdiri dari user interface, user experience, fungsionalitas dan work scurity. Metode yang digunakan meliputi klasifikasi sentimen dan aspek dengan metode deep learning (CNN,GRU dan TCN). Data primer bersumber dari hasil ulasan aplikasi pedulilindungi dengan teknik scraping pada situs https://www.pedulilindungi.id/. Hasil penelitian menunjukan bahwa terdapat enam aspek klasisifikasi sentimen pada aplikasi pedulilindungi yaitu aplikasi, user interface, user experience, kode OTP, cek sertifikat vaksin, bukti akses layanan. Hasil penelitian juga menunjukan bahwa metode CNN memperoleh nilai skor akurasi terbaik pada klasifikasi sentimen sebesar 98% dan klasifikasi aspek sebesar 97%.
PENERAPANAN MODEL DETEKSI OBJEK UNTUK ROBOT MENGGUNAKAN MODEL SSD DI LINGKUNGAN SIMULASI ROS Tamadjoe, Ilham Rizqyakbar; Dewa, Chandra Kusuma
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.8679

Abstract

The rapid advancement of automation technologies, particularly robotics, has transformed industries and economies in Southeast Asia. As Indonesia aims for developed nation status by 2045, embracing automation is crucial for competitiveness. However, Indonesia trails behind its regional peers in automation and robotics adoption, as noted by the International Federation of Robotics. Initiatives like the Indonesian Robotics Contest (KRI) seek to bridge this gap by fostering innovation among students and preparing them for international competitions like RoboCup. Challenges in KRI, such as object detection using color segmentation methods, highlight the need for more advanced computer vision techniques, particularly through deep learning. This paper proposes using a single shot detector model in robotic soccer within the ROS Gazebo simulation environment. Models like MobileNet V2 offer real-time object detection essential for autonomous robotics. By focusing on predefined objects like balls and goals, the study aims to develop a robotic system capable of accurately identifying and measuring distances to objects based on visual characteristics. This research aims to enhance Indonesia’s robotics capabilities, address limitations in object recognition, and advance automation technology in the region.
Enhancing Disease Diagnosis Coding: A Deep Learning Approach with Bidirectional GRU For ICD-10 Classification Priwibowo, Aqge; Dewa, Chandra Kusuma; Luthfi, Ahmad
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1320

Abstract

The health insurance claim in hospitals involves selecting specific ICD-10 codes for primary diagnosis texts. With rising claim volumes, the need for faster, more accurate coding is critical. This study develops a deep learning model to classify diagnosis texts into relevant ICD-10 codes using 9,982 original medical records from a national referral hospital under the Indonesian Ministry of Health. The classification method employs a BiGRU layer architecture, known for its effectiveness in handling sequential data, such as diagnosis texts. BiGRU operates bidirectionally, enhancing the model’s ability to capture the context from both past and future sequences. In this architecture, the BiGRU layer serves as the classification layer, stacked above the BERT layer, which functions as the vector embedding layer, converting text into numerical representations for the model. The results of the study demonstrate a promising solution for codifying primary diagnosis texts, achieving a precision of 82.18% and a recall of 81.59%. Despite the strong performance of the model, further improvements are possible. Interestingly, the study also observed that the size of the class volume per ICD-10 code is not the only factor affecting classification performance, as some classes with smaller volumes exhibited better classification results. However, merging rare classes did not improve performance and even worsened it, suggesting that better ways to handle underrepresented classes are needed. Experiments with different embedding layers, such as IndoBERT and BioClinicalBERT, and hyperparameter tuning yielded minimal performance gains, suggesting the need for alternative optimization strategies.
Sentiment Analysis On Indonesian Tweets about the 2024 Election Sembiring, Alfan Ramadhan; Dewa, Chandra Kusuma
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14481

Abstract

This study investigates public sentiment on Indonesian Twitter regarding the 2024 General Election, employing machine learning and deep learning techniques, including Naïve Bayes, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The dataset was collected using a Tweet Harvest method with the keyword "Pemilu" and underwent preprocessing steps such as case folding, removal of symbols and URLs, stopword elimination, and tokenization to ensure data quality. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were applied to assess the models' effectiveness. Naïve Bayes achieved the highest accuracy of 64%, followed by SVM at 63%, LSTM at 60%, and GRU at 57%. The findings indicate that traditional models like Naïve Bayes and SVM perform effectively on smaller datasets with structured features, while deep learning models excel in capturing complex sequential dependencies. However, deep learning methods exhibited overfitting tendencies, indicating the need for better regularization and optimization techniques. Furthermore, it emphasizes the potential of integrating traditional algorithms with advanced methods to enhance sentiment classification accuracy and generalizability across diverse datasets.
PREDICTING FANTASY PREMIER LEAGUE POINTS USING CONVOLUTIONAL NEURAL NETWORK AND LONG SHORT TERM MEMORY Lombu, Anas Satria; Paputungan, Irving Vitra; Dewa, Chandra Kusuma
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1792

Abstract

Fantasy Premier League is a fantasy sports-based game focused on football, particularly the English Premier League. each manager in this game is given the opportunity to build a virtual team for one season. A virtual team consists of various player positions that will earn FPL points based on their real-word performance. This research aims to implement a deep learning algorithm to predict FPL points generated by players based on their performance in the last 5 matches using a dataset collected from August 14, 2021, to May 21, 2022. The prediction model is designed using a Convolutional Neural Network algorithm consisting of one-dimensional Convolution layers, Max Pooling, and Dense layers. Additionally, a Long Short Term Memory algorithm with LSTM layers and Dense layers totaling 64 units is added as a comparison model to determine the best performing deep learning model in this study. In the first scenario, with a 70:30 data ratio, the average Mean Squared Error values obtained for 4 player positions using CNN are 0.0052 and 0.0027 for LSTM. Meanwhile, in the second scenario with an 80:20 data ratio, the evaluation results are 0.0027 for CNN and 0.0022 for LSTM. the model evaluation results indicate that the LSTM algorithm, utilizing three gates in the model architecture, is superior in recognizing historical data sequences compared to the CNN algorithm.
Co-Authors Ahmad Luthfi Asriny, Novendri Isra Fudholi, Dhomas Hatta Lombu, Anas Satria Luthfi, Ahmat Nur Iman Paputungan, Irving Vitra Priwibowo, Aqge Sembiring, Alfan Ramadhan Suryatin, Suryatin Tamadjoe, Ilham Rizqyakbar